



'''simple docstring'''

import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE__     :						Tuple	=  Mapping[str, Any]  # Is a nested dict.
SCREAMING_SNAKE_CASE__     :						int	=  0.01

@dataclasses.dataclass(frozen=snake_case__     )
class 							a__:
			a_	: np.ndarray  # [num_res, num_atom_type, 3]

			# Amino-acid type for each residue represented as an integer between 0 and
			# 20, where 20 is 'X'.
			a_	: np.ndarray  # [num_res]

			# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
			# is present and 0.0 if not. This should be used for loss masking.
			a_	: np.ndarray  # [num_res, num_atom_type]

			# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
			a_	: np.ndarray  # [num_res]

			# B-factors, or temperature factors, of each residue (in sq. angstroms units),
			# representing the displacement of the residue from its ground truth mean
			# value.
			a_	: np.ndarray  # [num_res, num_atom_type]

			# Chain indices for multi-chain predictions
			a_	: Optional[np.ndarray]									=      None

			# Optional remark about the protein. Included as a comment in output PDB
			# files
			a_	: Optional[str]									=      None

			# Templates used to generate this protein (prediction-only)
			a_	: Optional[Sequence[str]]									=      None

			# Chain corresponding to each parent
			a_	: Optional[Sequence[int]]									=      None






def 				a  (     UpperCamelCase_    :    str			)       ->	Protein:
		snake_case__	     =r'(\[[A-Z]+\]\n)'
		snake_case__	     =[tag.strip() for tag in re.split(UpperCamelCase_      ,					UpperCamelCase_			) if len(UpperCamelCase_			) > 0]
		snake_case__	     =zip(tags[0::2]      ,					[l.split('\n'			) for l in tags[1::2]]			)

		snake_case__	     =["N", "CA", "C"]
		snake_case__	     =None
		snake_case__	     =None
		snake_case__	     =None
		for g in groups:
				if "[PRIMARY]" == g[0]:
						snake_case__	     =g[1][0].strip()
						for i in range(len(UpperCamelCase_			)			):
								if seq[i] not in residue_constants.restypes:
										snake_case__	     ='X'  # FIXME: strings are immutable
						snake_case__	     =np.array(
						    [residue_constants.restype_order.get(UpperCamelCase_      ,					residue_constants.restype_num			) for res_symbol in seq]			)
				elif "[TERTIARY]" == g[0]:
						snake_case__	     =[]
						for axis in range(3			):
								tertiary.append(list(map(UpperCamelCase_      ,					g[1][axis].split()			)			)			)
						snake_case__	     =np.array(UpperCamelCase_			)
						snake_case__	     =np.zeros((len(tertiary[0]			) // 3, residue_constants.atom_type_num, 3)			).astype(np.floataa			)
						for i, atom in enumerate(UpperCamelCase_			):
								snake_case__	     =np.transpose(tertiary_np[:, i::3]			)
						atom_positions *= PICO_TO_ANGSTROM
				elif "[MASK]" == g[0]:
						snake_case__	     =np.array(list(map({'-': 0, '+': 1}.get      ,					g[1][0].strip()			)			)			)
						snake_case__	     =np.zeros(
						    (
						        len(UpperCamelCase_			),
						        residue_constants.atom_type_num,
						    )			).astype(np.floataa			)
						for i, atom in enumerate(UpperCamelCase_			):
								snake_case__	     =1
						atom_mask *= mask[..., None]

		assert aatype is not None

		return Protein(
		    atom_positions=UpperCamelCase_      ,					atom_mask=UpperCamelCase_      ,					aatype=UpperCamelCase_      ,					residue_index=np.arange(len(UpperCamelCase_			)			)      ,					b_factors=UpperCamelCase_      ,					)






def 				a  (     UpperCamelCase_    :    Protein      ,					UpperCamelCase_    :    int = 0			)       ->	List[str]:
		snake_case__	     =[]

		snake_case__	     =prot.remark
		if remark is not None:
				pdb_headers.append(f"""REMARK {remark}"""			)

		snake_case__	     =prot.parents
		snake_case__	     =prot.parents_chain_index
		if parents is not None and parents_chain_index is not None:
				snake_case__	     =[p for i, p in zip(UpperCamelCase_      ,					UpperCamelCase_			) if i == chain_id]

		if parents is None or len(UpperCamelCase_			) == 0:
				snake_case__	     =['N/A']

		pdb_headers.append(f"""PARENT {' '.join(UpperCamelCase_			)}"""			)

		return pdb_headers






def 				a  (     UpperCamelCase_    :    Protein      ,					UpperCamelCase_    :    str			)       ->	str:
		snake_case__	     =[]
		snake_case__	     =pdb_str.split('\n'			)

		snake_case__	     =prot.remark
		if remark is not None:
				out_pdb_lines.append(f"""REMARK {remark}"""			)

		snake_case__	     =42
		if prot.parents is not None and len(prot.parents			) > 0:
				snake_case__	     =[]
				if prot.parents_chain_index is not None:
						snake_case__	     ={}
						for p, i in zip(prot.parents      ,					prot.parents_chain_index			):
								parent_dict.setdefault(str(UpperCamelCase_			)      ,					[]			)
								parent_dict[str(UpperCamelCase_			)].append(UpperCamelCase_			)

						snake_case__	     =max([int(UpperCamelCase_			) for chain_idx in parent_dict]			)
						for i in range(max_idx + 1			):
								snake_case__	     =parent_dict.get(str(UpperCamelCase_			)      ,					['N/A']			)
								parents_per_chain.append(UpperCamelCase_			)
				else:
						parents_per_chain.append(list(prot.parents			)			)
		else:
				snake_case__	     =[['N/A']]

		def make_parent_line(UpperCamelCase_    :    Sequence[str]			) -> str:
				return f"""PARENT {' '.join(UpperCamelCase_			)}"""

		out_pdb_lines.append(make_parent_line(parents_per_chain[0]			)			)

		snake_case__	     =0
		for i, l in enumerate(UpperCamelCase_			):
				if "PARENT" not in l and "REMARK" not in l:
						out_pdb_lines.append(UpperCamelCase_			)
				if "TER" in l and "END" not in lines[i + 1]:
						chain_counter += 1
						if not chain_counter >= len(UpperCamelCase_			):
								snake_case__	     =parents_per_chain[chain_counter]
						else:
								snake_case__	     =['N/A']

						out_pdb_lines.append(make_parent_line(UpperCamelCase_			)			)

		return "\n".join(UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    Protein			)       ->	str:
		snake_case__	     =residue_constants.restypes + ['X']

		def res_atoa(UpperCamelCase_    :    int			) -> str:
				return residue_constants.restype_atoa.get(restypes[r]      ,					'UNK'			)

		snake_case__	     =residue_constants.atom_types

		snake_case__	     =[]

		snake_case__	     =prot.atom_mask
		snake_case__	     =prot.aatype
		snake_case__	     =prot.atom_positions
		snake_case__	     =prot.residue_index.astype(np.intaa			)
		snake_case__	     =prot.b_factors
		snake_case__	     =prot.chain_index

		if np.any(aatype > residue_constants.restype_num			):
				raise ValueError('Invalid aatypes.'			)

		snake_case__	     =get_pdb_headers(UpperCamelCase_			)
		if len(UpperCamelCase_			) > 0:
				pdb_lines.extend(UpperCamelCase_			)

		snake_case__	     =aatype.shape[0]
		snake_case__	     =1
		snake_case__	     =0
		snake_case__	     =string.ascii_uppercase
		snake_case__	     =None
		# Add all atom sites.
		for i in range(UpperCamelCase_			):
				snake_case__	     =res_atoa(aatype[i]			)
				for atom_name, pos, mask, b_factor in zip(UpperCamelCase_      ,					atom_positions[i]      ,					atom_mask[i]      ,					b_factors[i]			):
						if mask < 0.5:
								continue

						snake_case__	     ='ATOM'
						snake_case__	     =atom_name if len(UpperCamelCase_			) == 4 else f""" {atom_name}"""
						snake_case__	     =''
						snake_case__	     =''
						snake_case__	     =1.0_0
						snake_case__	     =atom_name[0]  # Protein supports only C, N, O, S, this works.
						snake_case__	     =''

						snake_case__	     ='A'
						if chain_index is not None:
								snake_case__	     =chain_tags[chain_index[i]]

						# PDB is a columnar format, every space matters here!
						snake_case__	     =(
						    f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
						    f"""{res_name_a:>3} {chain_tag:>1}"""
						    f"""{residue_index[i]:>4}{insertion_code:>1}   """
						    f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
						    f"""{occupancy:>6.2f}{b_factor:>6.2f}          """
						    f"""{element:>2}{charge:>2}"""
						)
						pdb_lines.append(UpperCamelCase_			)
						atom_index += 1

				snake_case__	     =i == n - 1
				if chain_index is not None:
						if i != n - 1 and chain_index[i + 1] != prev_chain_index:
								snake_case__	     =True
								snake_case__	     =chain_index[i + 1]

				if should_terminate:
						# Close the chain.
						snake_case__	     ='TER'
						snake_case__	     =(
						    f"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]			):>3} {chain_tag:>1}{residue_index[i]:>4}"""
						)
						pdb_lines.append(UpperCamelCase_			)
						atom_index += 1

						if i != n - 1:
								# "prev" is a misnomer here. This happens at the beginning of
								# each new chain.
								pdb_lines.extend(get_pdb_headers(UpperCamelCase_      ,					UpperCamelCase_			)			)

		pdb_lines.append('END'			)
		pdb_lines.append(''			)
		return "\n".join(UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    Protein			)       ->	np.ndarray:
		return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def 				a  (     UpperCamelCase_    :    FeatureDict      ,					UpperCamelCase_    :    ModelOutput      ,					UpperCamelCase_    :    Optional[np.ndarray] = None      ,					UpperCamelCase_    :    Optional[np.ndarray] = None      ,					UpperCamelCase_    :    Optional[str] = None      ,					UpperCamelCase_    :    Optional[Sequence[str]] = None      ,					UpperCamelCase_    :    Optional[Sequence[int]] = None      ,					)       ->	Protein:
		return Protein(
		    aatype=features['aatype']      ,					atom_positions=result['final_atom_positions']      ,					atom_mask=result['final_atom_mask']      ,					residue_index=features['residue_index'] + 1      ,					b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask']			)      ,					chain_index=UpperCamelCase_      ,					remark=UpperCamelCase_      ,					parents=UpperCamelCase_      ,					parents_chain_index=UpperCamelCase_      ,					)









'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




